Local Structure and Crystallization Transformation of Hydrous Ferric Arsenate in Acidic H2O-Fe(III)-As(V)-SO42- Systems: Implications for Acid Mine Drainage and Arsenic Geochemical Cycling.

Hydrous ferric arsenate (HFA) is a common thermodynamically metastable phase in acid mine drainage (AMD). However, little is known regarding the structural forms and transformation mechanism of HFA. We investigated the local atomic structures and the crystallization transformation of HFA at various Fe(III)/As(V) ratios (2, 1, 0.5, 0.33, and 0.25) in acidic solutions (pH 1.2 and 1.8). The results show that the Fe(III)/As(V) in HFA decreases with decreasing initial Fe(III)/As(V) at acidic pHs. The degree of protonation of As(V) in HFA increases with increasing As(V) concentrations. The Fe K-edge extended X-ray absorption fine structure and X-ray absorption near-edge structure results reveal that each FeO6 is linked to more than two AsO4 in HFA precipitated at Fe(III)/As(V) < 1. Furthermore, the formation of scorodite (FeAsO4·2H2O) is greatly accelerated by decreasing the initial Fe(III)/As(V). The release of As(V) from HFA is observed during its crystallization transformation process to scorodite at Fe(III)/As(V) < 1, which is different from that at Fe(III)/As(V) ≥ 1. Scanning electron microscopy results show that Oswald ripening is responsible for the coarsening of scorodite regardless of the initial Fe(III)/As(V) or pH. Moreover, the formation of crystalline ferric dihydrogen arsenate as an intermediate phase at Fe(III)/As(V) < 1 is responsible for the enhanced transformation rate from HFA to scorodite. This work provides new insights into the local atomic structure of HFA and its crystallization transformation that may occur in AMD and has important implications for arsenic geochemical cycling.

Develop prediction model to help forecast advanced prostate cancer patients' prognosis after surgery using neural network.

Background: The effect of surgery on advanced prostate cancer (PC) is unclear and predictive model for postoperative survival is lacking yet. Methods: We investigate the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) database, to collect clinical features of advanced PC patients. According to clinical experience, age, race, grade, pathology, T, N, M, stage, size, regional nodes positive, regional nodes examined, surgery, radiotherapy, chemotherapy, history of malignancy, clinical Gleason score (composed of needle core biopsy or transurethral resection of the prostate specimens), pathological Gleason score (composed of prostatectomy specimens) and prostate-specific antigen (PSA) are the potential predictive variables. All samples are divided into train cohort (70% of total, for model training) and test cohort (30% of total, for model validation) by random sampling. We then develop neural network to predict advanced PC patients' overall. Area under receiver operating characteristic curve (AUC) is used to evaluate model's performance. Results: 6380 patients, diagnosed with advanced (stage III-IV) prostate cancer and receiving surgery, have been included. The model using all collected clinical features as predictors and based on neural network algorithm performs best, which scores 0.7058 AUC (95% CIs, 0.7021-0.7068) in train cohort and 0.6925 AUC (95% CIs, 0.6906-0.6956) in test cohort. We then package it into a Windows 64-bit software. Conclusion: Patients with advanced prostate cancer may benefit from surgery. In order to forecast their overall survival, we first build a clinical features-based prognostic model. This model is accuracy and may offer some reference on clinical decision making.

Eukaryotic translation initiation factor 4A1 in the pathogenesis and treatment of cancers.

Abnormal translate regulation is an important phenomenon in cancer initiation and progression. Eukaryotic translation initiation factor 4A1 (eIF4A1) protein is an ATP-dependent Ribonucleic Acid (RNA) helicase, which is essential for translation and has bidirectional RNA unwinders function. In this review, we discuss the levels of expression, regulatory mechanisms and protein functions of eIF4A1 in different human tumors. eIF4A1 is often involved as a target of microRNAs or long non-coding RNAs during the epithelial-mesenchymal transition, associating with the proliferation and metastasis of tumor cells. eIF4A1 protein exhibits the promising biomarker for rapid diagnosis of pre-cancer lesions, histological phenotypes, clinical staging diagnosis and outcome prediction, which provides a novel strategy for precise medical care and target therapy for patients with tumors at the same time, relevant small molecule inhibitors have also been applied in clinical practice, providing reliable theoretical support and clinical basis for the development of this gene target.

A deep learning-based model (DeepMPM) to help predict survival in patients with malignant pleural mesothelioma.

Background: Malignant pleural mesothelioma (MPM) is a rare disease with limited treatment and poor prognosis, and a precise and reliable means to predicting MPM remains lacking for clinical use. Methods: In the population-based cohort study, we collected clinical characteristics from the Surveillance, Epidemiology, and End Results (SEER) database. According to the time of diagnosis, the SEER data were divided into 2 cohorts: the training cohort (from 2010 to 2016) and the test cohort (from 2017 to 2019). The training cohort was used to train a deep learning-based predictive model derived from DeepSurv theory, which was validated by both the training and the test cohorts. All clinical characteristics were included and analyzed using Cox proportional risk regression or Kaplan-Meier curve to determine the risk factors and protective factors of MPM. Results: The survival model included 3,130 cases (2,208 in the training cohort and 922 in the test cohort). As for model's performance, the area under the receiver operating characteristics curve (AUC) was 0.7037 [95% confidence interval (CI): 0.7030-0.7045] in the training cohort and 0.7076 (95% CI: 0.7067-0.7086) in the test cohort. Older age; male sex, sarcomatoid mesothelioma; and T4, N2, and M1 stage tended to be the risk factors for survival. Meanwhile, epithelioid mesothelioma, surgery, radiotherapy, and chemotherapy tended to be the protective factors. The median overall survival (OS) of patients who underwent surgery combined with radiotherapy was the longest, followed by those who underwent a combination of surgery, radiotherapy, and chemotherapy. Conclusions: Our deep learning-based model precisely could predict the survival of patients with MPM; moreover, multimode combination therapy might provide more meaningful survival benefits.

Human umbilical cord mesenchymal stem cells alleviated TNBS-induced colitis in mice by restoring the balance of intestinal microbes and immunoregulation.

AIMS: Human umbilical cord mesenchymal stem cells (HUMSCs) have been documented to be effective for several immune disorders including inflammatory bowel diseases (IBD). However, it remains unclear how HUMSCs function in regulating immune responses and intestinal flora in the trinitrobenzene sulfonic acid (TNBS)-induced IBD model. MATERIALS AND METHODS: We assessed the regulatory effects of HUMSCs on the gut microbiota, T lymphocyte subpopulations and related immune cytokines in the TNBS-induced IBD model. The mice were divided into the normal, TNBS, and HUMSC-treated groups. The effect of HUMSCs was evaluated by Hematoxylin and Eosin (H&E) staining, fluorescence-activated cell sorting (FACS), and enzyme-linked immunosorbent assay (ELISA) analyses. Metagenomics Illumina sequencing was conducted for fecal samples. KEY FINDINGS: We demonstrated that the disease symptoms and pathological changes in the colon tissues of TNBS-induced colitis mice were dramatically ameliorated by HUMSCs, which improved the gut microbiota and rebalanced the immune system, increasing the abundance of healthy bacteria (such as Lactobacillus murinus and Lactobacillus johnsonii), the Firmicutes/Bacteroidetes ratio, and the proportion of Tregs; the Th1/Th17 ratio was decreased. Consistently, the expression levels of IFN-γ and IL-17 were significantly decreased, and transforming growth factor-ß1 (TGF-ß1) levels were significantly increased in the plasma of colitis mice HUMSC injection. SIGNIFICANCE: Our experiment revealed that HUMSCs mitigate acute colitis by regulating the rebalance of Th1/Th17/Treg cells and related cytokines and remodeling the gut microbiota, providing potential future therapeutic targets in IBD.

Direct immobilization of Se(IV) from acidic Se(IV)-rich wastewater via ferric selenite Co-precipitation.

The attenuation of acidic Se(IV)-rich wastewater, including those associated with acid mine drainage (AMD) and nonferrous metallurgical wastewater (NMW), presents a serious environmental challenge. This study investigates the effects of diverse factors from pH values to Se(IV)/Fe(III) molar ratios, initial Se(IV) concentrations, and alkali neutralization agents on the direct co-precipitation of ferric selenites in AMD and NMW systems involving different orders of Fe(III) and alkali addition. Our results show that amorphous sulfate-substituted ferric (hydrogen) selenite and Se(IV)-bearing ferrihydrite-schwertmannite are the major Se(IV)-attenuation solids except that gypsum is an additional phase in the NMW system with Ca(OH)2 neutralization. Produced ferric selenites achieve 98-99.8% of Se(IV) immobilization under optimal conditions of pH 4.5, Se(IV)/Fe(III) molar ratios of 0.0625-0.5, and initial Se(IV) concentrations of 0.15-1.3 mmol·L-1. Moreover, completing FeSO4+ and FeHSeO32+/FeSeO3+ complexes as well as different ferric selenite co-precipitates are shown to collectively control aqueous Se(IV) remaining. Specifically, three distinct trends of aqueous Se(IV) concentrations separately correspond to changes in the four factors. The co-precipitation in the NMW system via pH adjustment followed by Fe(III) addition is more efficient for Se(IV) fixation than that in the AMD system because of minimal complexation, concurrent Fe(III) hydrolysis, and enhanced ferric selenite co-precipitation in the former.

Serum metabolomics of hyperbilirubinemia and hyperuricemia in the Tibetan plateau has unique characteristics.

Few studies have provided data on the metabolomics characteristics of metabolic diseases such as hyperuricemia and hyperbilirubinemia in the Tibetan plateau. In the current study, we sought to investigate the serum metabolomics characteristics of hyperbilirubinemia and hyperuricemia in the Tibetan plateau, with the aim to provide a basis for further research on their pathogenesis, prevention, and treatment. The study participants were born in low-altitude areas below 1000 m and had no prior experience living in a high-altitude area before entering Golmud, Tibet (average elevation: 3000 m) and Yushu, Qinghai (average elevation: 4200 m). Thirty-four participants with hyperbilirubinemia (18 in Golmud and 16 in Yushu), 24 participants with hyperuricemia, and 22 healthy controls were enrolled. The serum samples of subjects were separated and then sent to a local tertiary hospital for biochemical examination. Serum widely targeted technology, based on the ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) platform, was used to detect serum metabolites and differential metabolites. Compared to the healthy controls, hyperbilirubinemia patients from Golmud showed 19 differential metabolites, hyperbilirubinemia patients from Yushu showed 12 differential metabolites, and hyperuricemia patients from Yushu showed 23 differential metabolites. Compared to the hyperbilirubinemia patients from Golmud that is at a low altitude, the Yushu groups had 33 different metabolites. Differential metabolites are primarily classified into amino acids and their derivatives, nucleotides and their derivatives, organic acids and their derivatives, and lipids/fatty acids. These are related to metabolic pathways such as caffeine metabolism, arachidonic acid metabolism, and tyrosine metabolism. Hyperbilirubinemia and hyperuricemia in the Tibetan plateau have unique serum metabolomics characteristics. Glycine derivatives and arachidonic acid and its derivatives were associated with plateau hyperbilirubinemia, and vanillic acid and pentadecafluorooctanoic acid were associated with plateau hyperuricemia.

Neural network-based prognostic predictive tool for gastric cardiac cancer: the worldwide retrospective study.

BACKGROUNDS: The incidence of gastric cardiac cancer (GCC) has obviously increased recently with poor prognosis. It's necessary to compare GCC prognosis with other gastric sites carcinoma and set up an effective prognostic model based on a neural network to predict the survival of GCC patients. METHODS: In the population-based cohort study, we first enrolled the clinical features from the Surveillance, Epidemiology and End Results (SEER) data (n = 31,397) as well as the public Chinese data from different hospitals (n = 1049). Then according to the diagnostic time, the SEER data were then divided into two cohorts, the train cohort (patients were diagnosed as GCC in 2010-2014, n = 4414) and the test cohort (diagnosed in 2015, n = 957). Age, sex, pathology, tumor, node, and metastasis (TNM) stage, tumor size, surgery or not, radiotherapy or not, chemotherapy or not and history of malignancy were chosen as the predictive clinical features. The train cohort was utilized to conduct the neural network-based prognostic predictive model which validated by itself and the test cohort. Area under the receiver operating characteristics curve (AUC) was used to evaluate model performance. RESULTS: The prognosis of GCC patients in SEER database was worse than that of non GCC (NGCC) patients, while it was not worse in the Chinese data. The total of 5371 patients were used to conduct the model, following inclusion and exclusion criteria. Neural network-based prognostic predictive model had a satisfactory performance for GCC overall survival (OS) prediction, which owned 0.7431 AUC in the train cohort (95% confidence intervals, CI, 0.7423-0.7439) and 0.7419 in the test cohort (95% CI, 0.7411-0.7428). CONCLUSIONS: GCC patients indeed have different survival time compared with non GCC patients. And the neural network-based prognostic predictive tool developed in this study is a novel and promising software for the clinical outcome analysis of GCC patients.

Interpretable deep learning survival predictive tool for small cell lung cancer.

Background: Small cell lung cancer (SCLC) is an aggressive and almost universally lethal neoplasm. There is no accurate predictive method for its prognosis. Artificial intelligence deep learning may bring new hope. Methods: By searching the Surveillance, Epidemiology, and End Results database (SEER), 21,093 patients' clinical data were eventually included. Data were then divided into two groups (train dataset/test dataset). The train dataset (diagnosed in 2010-2014, N = 17,296) was utilized to conduct a deep learning survival model, validated by itself and the test dataset (diagnosed in 2015, N = 3,797) in parallel. According to clinical experience, age, sex, tumor site, T, N, M stage (7th American Joint Committee on Cancer TNM stage), tumor size, surgery, chemotherapy, radiotherapy, and history of malignancy were chosen as predictive clinical features. The C-index was the main indicator to evaluate model performance. Results: The predictive model had a 0.7181 C-index (95% confidence intervals, CIs, 0.7174-0.7187) in the train dataset and a 0.7208 C-index (95% CIs, 0.7202-0.7215) in the test dataset. These indicated that it had a reliable predictive value on OS for SCLC, so it was then packaged as a Windows software which is free for doctors, researchers, and patients to use. Conclusion: The interpretable deep learning survival predictive tool for small cell lung cancer developed by this study had a reliable predictive value on their overall survival. More biomarkers may help improve the prognostic predictive performance of small cell lung cancer.

Ex Situ Reconstruction-Shaped Ir/CoO/Perovskite Heterojunction for Boosted Water Oxidation Reaction.

The oxygen evolution reaction (OER) is the performance-limiting step in the process of water splitting. In situ electrochemical conditioning could induce surface reconstruction of various OER electrocatalysts, forming reactive sites dynamically but at the expense of fast cation leaching. Therefore, achieving simultaneous improvement in catalytic activity and stability remains a significant challenge. Herein, we used a scalable cation deficiency-driven exsolution approach to ex situ reconstruct a homogeneous-doped cobaltate precursor into an Ir/CoO/perovskite heterojunction (SCI-350), which served as an active and stable OER electrode. The SCI-350 catalyst exhibited a low overpotential of 240 mV at 10 mA cm-2 in 1 M KOH and superior durability in practical electrolysis for over 150 h. The outstanding activity is preliminarily attributed to the exponentially enlarged electrochemical surface area for charge accumulation, increasing from 3.3 to 175.5 mF cm-2. Moreover, density functional theory calculations combined with advanced spectroscopy and 18O isotope-labeling experiments evidenced the tripled oxygen exchange kinetics, strengthened metal-oxygen hybridization, and engaged lattice oxygen oxidation for O-O coupling on SCI-350. This work presents a promising and feasible strategy for constructing highly active oxide OER electrocatalysts without sacrificing durability.

Industrial-scale extraction of high value-added kaolin from excavation waste: Demonstration from Xiamen, China.

Excavation waste from the construction of subways and other underground infrastructures is mainly composed of gravel, sand and clay of minimal economic value, which commonly ends up in landfills. Although the coarse sand and gravel of the excavation waste are typically recycled on site, a large amount of the fine-grained residue must be disposed of due to the prohibition of marine land reclamation in Xiamen, China, leading to an increasingly severe shortage of landfills. In this contribution, a new strategy was successfully developed for industrial-scale extraction of high value-added kaolin from the excavation waste of Xiamen. This strategy can overcome the challenges of complex and variable chemical compositions, high iron contents, low industrial grade, and organic contaminants in the raw materials. Characterization using chemical analysis, powder X-ray diffraction, scanning electron microscopy, and infrared spectroscopy showed that the Xiamen excavation waste originated from granite weathering is mainly composed of kaolinite and quartz, along with high Fe contents and other impurities. The excavation waste was subjected to an intensive process of blunging, grinding, sieving, and classifying, as well as successive iron removal by magnetic separation. Subsequently, the extracted products meet commercial requirements, including those for high-quality kaolin with whiteness and plasticity larger than 90° and 17%, respectively. Moreover, an industrial-scale green production line with an annual treatment capacity of one million tonnes of excavation waste at the utilization rate of 100% was implemented. Hence, this work presents an effective approach for exploiting similar excavation waste around the world to promote sustainable development.

Deep learning survival model for colorectal cancer patients (DeepCRC) with Asian clinical data compared with different theories.

Introduction: Colorectal cancer (CRC) is the third most common cancer. Precise prediction of CRC patients' overall survival (OS) probability could offer advice on its treatment. Neural network (NN) is the first-class algorithm, but a consensus on which NN survival models are better has not been established yet. A predictive model on CRC using Asian data is also lacking. Methods: We conducted 8 NN survival models of CRC (n = 416) with different theories and compared them using Asian data. Results: DeepSurv performed best with a C-index value of 0.8300 in the training cohort and 0.7681 in the test cohort. Conclusions: The deep learning survival model for CRC patients (DeepCRC) could predict CRC's OS accurately.

Altered gut microbiome composition in nontreated plaque psoriasis patients.

Recent studies have demonstrated that dysbiosis of the gut microbiota is associated with psoriasis, but these studies showed some conflicting results. Our study examined differences in microbiome composition associated in people with psoriasis and those without. Comparing individuals with their healthy partners was a second strategy. We explored the fecal microbiota among 32 nontreated plaque psoriasis patients, 15 healthy controls and 17 healthy couples by metagenomic gene sequencing. The relative levels of intestinal microbiota of the psoriasis cohort differed from those in healthy controls and these patients' partners. However, there was no microbial diversity among these three cohorts. On the level of the phylum, Firmicutes and Bacteroidetes' relative abundances were reversed. Escherichia coli was significantly enriched in the psoriasis group compared with the healthy people and the healthy spouses. Gene functional analysis indicated that Ribosome (ko03010) was upregulated, Flagellar assembly (ko02040) and Bacterial chemotaxis (ko02030) were downregulated in the psoriasis cohort compared with the healthy individuals and the healthy spouses. The microbiota in severe psoriasis patients differed from those with milder conditions. These findings strongly support the association between intestinal flora and psoriasis. It is necessary to perform more meaningful experiments to identify whether the differences of gut microbiota are the cause or consequences of psoriasis in future.

Serum metabolite signatures in normal individuals and patients with colorectal adenoma or colorectal cancer using UPLC-MS/MS method.

Colorectal cancer (CRC) is one of the main causes of cancer-related deaths worldwide. Sporadic CRC develops from normal mucosa via adenoma to adenocarcinoma, which provides a long screening window for clinical detection. However, early diagnosis of sporadic colorectal adenoma (CRA) and CRC using serum metabolic screening remains unclear. The purpose of this study was to identify some promising signatures for distinguishing the different pathological metabolites of colorectal mucosal malignant transformation. A total of 238 endogenous metabolites were elected. We found that CRA and CRC patients had 72 and 73 different metabolites compared with healthy controls, respectively. There were 20 different metabolites between CRA and CRC patients. The potential metabolites of tumor growth (including patients with CRA and CRC) were found, such as A-d-glucose, D-mannose, N-acetyl-D-glucosamine, L-cystine, Sarcosine, TXB 2, 12-Hete, and chenodeoxycholic acid. Compared with CRA, 3,4,5-trimethoxybenzoic acid was significantly higher in CRC patients. There results prompt us to use the potential serum signatures to screen CRC as the novel strategy. Serum metabolite screening is useful for early detection of mucosal intestinal malignancy. We will further investigate the roles of these promising biomarkers during intestinal tumorigenesis in future. SIGNIFICANCE: CRC is one of the main causes of cancer-related deaths worldwide. Sporadic CRC develops from normal mucosa via adenomas to adenocarcinoma, which provides a long screening window for about 5-10 years. We adopt the metabolic analysis of extensive targeted metabolic technology. The main purpose of the metabolic group analysis is to detect and screen the different metabolites, thereby performing related functional prediction and analysis of the differential metabolites. In our study, 30 samples are selected, divided into 3 groups for metabolic analysis, and 238 metabolites are elected. In 238 metabolites, we find that CRA patients have 72 different metabolites compared with health control. Compared with health control, CRC have 73 different metabolites. Compared with CRA and CRC patients, there are 20 different metabolites. The annotation results of the significantly different metabolites are classified according to the KEGG pathway type. The potential metabolites of tumor growth stage (including patients with CRA and CRC) are found, such as A-d-glucose, D-mannose, N-acetyl-D-glucosamine, L-cystine, sarcosine, TXB 2, 12-Hete and chenodeoxycholic acid. Compared with CRA patients, CRC patients had significantly higher 3,4,5-trimethoxybenzoic acid level. It is prompted to use serum different metabolites to screen CRC to provide new possibilities.

Deciphering complex antibiotic resistance patterns in Helicobacter pylori through whole genome sequencing and machine learning.

Introduction: Helicobacter pylori (H.pylori, Hp) affects billions of people worldwide. However, the emerging resistance of Hp to antibiotics challenges the effectiveness of current treatments. Investigating the genotype-phenotype connection for Hp using next-generation sequencing could enhance our understanding of this resistance. Methods: In this study, we analyzed 52 Hp strains collected from various hospitals. The susceptibility of these strains to five antibiotics was assessed using the agar dilution assay. Whole-genome sequencing was then performed to screen the antimicrobial resistance (AMR) genotypes of these Hp strains. To model the relationship between drug resistance and genotype, we employed univariate statistical tests, unsupervised machine learning, and supervised machine learning techniques, including the development of support vector machine models. Results: Our models for predicting Amoxicillin resistance demonstrated 66% sensitivity and 100% specificity, while those for Clarithromycin resistance showed 100% sensitivity and 100% specificity. These results outperformed the known resistance sites for Amoxicillin (A1834G) and Clarithromycin (A2147), which had sensitivities of 22.2% and 87%, and specificities of 100% and 96%, respectively. Discussion: Our study demonstrates that predictive modeling using supervised learning algorithms with feature selection can yield diagnostic models with higher predictive power compared to models relying on single single-nucleotide polymorphism (SNP) sites. This approach significantly contributes to enhancing the precision and effectiveness of antibiotic treatment strategies for Hp infections. The application of whole-genome sequencing for Hp presents a promising pathway for advancing personalized medicine in this context.

Assessing the efficacy of immunotherapy in lung squamous carcinoma using artificial intelligence neural network.

Background: At present, immunotherapy is a very promising treatment method for lung cancer patients, while the factors affecting response are still controversial. It is crucial to predict the efficacy of lung squamous carcinoma patients who received immunotherapy. Methods: In our retrospective study, we enrolled lung squamous carcinoma patients who received immunotherapy at Beijing Chest Hospital from January 2017 to November 2021. All patients were grouped into two cohorts randomly, the training cohort (80% of the total) and the test cohort (20% of the total). The training cohort was used to build neural network models to assess the efficacy and outcome of immunotherapy in lung squamous carcinoma based on clinical information. The main outcome was the disease control rate (DCR), and then the secondary outcomes were objective response rate (ORR), progression-free survival (PFS), and overall survival (OS). Results: A total of 289 patients were included in this study. The DCR model had area under the receiver operating characteristic curve (AUC) value of 0.9526 (95%CI, 0.9088-0.9879) in internal validation and 0.9491 (95%CI, 0.8704-1.0000) in external validation. The ORR model had AUC of 0.8030 (95%CI, 0.7437-0.8545) in internal validation and 0.7040 (95%CI, 0.5457-0.8379) in external validation. The PFS model had AUC of 0.8531 (95%CI, 0.8024-0.8975) in internal validation and 0.7602 (95%CI, 0.6236-0.8733) in external validation. The OS model had AUC of 0.8006 (95%CI, 0.7995-0.8017) in internal validation and 0.7382 (95%CI, 0.7366-0.7398) in external validation. Conclusions: The neural network models show benefits in the efficacy evaluation of immunotherapy to lung squamous carcinoma patients, especially the DCR and ORR models. In our retrospective study, we found that neoadjuvant and adjuvant immunotherapy may bring greater efficacy benefits to patients.

Facile synthesis of rose-like composites of zeolites and layered double hydroxides: Growth mechanism and enhanced properties.

Excellent performances of various materials often depend on high specific surface areas. Therefore, increase of specific surface areas is one of the most important means to improve the properties and performances of materials. Herein, we report a facile strategy to prepare novel composite materials of zeolites and hydrotalcite-like layered double hydroxides, with high specific surface areas. The composites with a rose-like morphology were synthesized hydrothermally by adding synthetic zeolites to the raw materials used for the formation of hydrotalcite. The resultant composites were shown to contain two distinct layered double hydroxides with different Mg/Al molar ratios. Nitrogen (N2) adsorption-desorption measurements showed that the specific surface areas and the pore volumes of these composites increased by an order of magnitude in comparison with hydrotalcite. The new composites were shown to be capable of effectively removing Cr(VI), Cu(II) and methylene blue from aqueous environments and had better performances for the latter two contaminants than hydrotalcite. Moreover, this strategy potentially opens up the synthesis of new composite materials with tunable compositions and enhanced properties for environmental and other applications.

SPAG5 Activates PI3K/AKT Pathway and Promotes the Tumor Progression and Chemo-Resistance in Gastric Cancer.

The sperm-associated antigen 5 (SPAG5) is an important protein in mitosis and cell cycle checkpoint regulation, with more attention as a novel oncogene in various cancers. High level of SPAG5 expression has been detected in our clinical gastric cancer (GC) samples and The Cancer Genome Atlas GC data. However, the bio-function and potential mechanism of SPAG5 in GC remain unclear. In this study, we investigated the role of SPAG5 in GC development and the correlation between SPAG5 and 5-fluorouracil (5-FU) treatment. SPAG5 expression was increased in GC samples compared with that in normal tissues (80.8% vs. 22.0%), which was apparently associated with a worse outcome. Biological experiments showed that knockdown of SPAG5 induced apoptosis and suppressed proliferation in cells and animal models. Downregulation of SPAG5 enhanced the sensitivity of 5-FU in GC cells. Gene microarray chip identified 856 upregulated and 787 downregulated genes in SPAG5 silencing cells. Furthermore, 12 significant genes, including CDKN1A, CDKN1B, EIF4E, MAPK1, and HSP90B1, belonged to the PI3K/AKT signaling pathway using ingenuity pathway analysis. Meanwhile, real-time PCR and Western blotting results showed that knockdown of SPAG5 inhibited PI3K/AKT signaling pathway. Collectively, SPAG5 promotes the growth of GC cells by regulating PI3K/AKT signaling pathway, which could be the promising target gene in GC therapy.

Iron(II)-activated phase transformation of Cd-bearing ferrihydrite: Implications for cadmium mobility and fate under anaerobic conditions.

The factors and mechanisms affecting the fate of the associated Cd during the Fe(II)-activated Cd-bearing ferrihydrite transformation remain poorly understood. Herein we have conducted a series of batch reactions containing ferrihydrite with diverse pH values and initial Fe(II) and Cd concentrations coupled with chemical analyses and spectroscopic examination on the transformation products to probe the mechanisms of the Cd partitioning and the processes of Fe(II)-activated Cd-bearing ferrihydrite transformation under anaerobic conditions. Chemical analyses, Fourier transform infrared spectroscopy (FTIR), and powder X-ray diffraction (PXRD) results show that the initial Fe(II) and Cd concentrations as well as pH values all have significant effects on the rates and pathways of ferrihydrite transformation. Increasing Cd loading enhances the inhibition of the Fe(II)-activated ferrihydrite transformation rates. High Cd loading alters the Fe(II)-activated ferrihydrite transformation pathways by hindering the recrystallization of both ferrihydrite to more stable iron minerals and the newly formed lepidocrocite to goethite. Chemical analyses show that the release of Cd to solutions during ferrihydrite transformation is accompanied by a reduction in the 0.4 M HCl extractable Cd fraction and that a significant amount of the released Cd is transformed to a 0.4 M HCl unextractable form. Moreover, enhanced Cd release during the Fe(II)-activated ferrihydrite transformation is observed by reducing the pH value or increasing the initial Cd concentration. Results from synchrotron X-ray absorption spectroscopy (XAS) confirm that the majority of the 0.4 M HCl unextractable Cd form is associated with structural incorporation into the recrystallized iron (hydr)oxides via isomorphous substitution for Fe(III). These findings not only provide molecular-level understanding on the behavior of Cd under natural anoxic environments, but also are useful in predicting the geochemical cycling of Cd and developing long-term Cd contaminant management strategies.